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Register a new userالمحاكاة و النمذجة
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Syrian Virtual University كتاب
Publication date
2018
fields
Informatics Engineering
and research's language is
العربية
Authors
رند القوتلي( باحث )
Created by
Shamra Editor
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تهدف المحاكاة باستخدام الحاسب إلى محاكاة أو تقليد نظم موجودة و مستخدمة في الواقع. لدراسة هذه النظم يجب وضع فرضيات حول طريقة عملها هذه الفرضيات تؤدي عادة إلى الحصول على معادلات رياضية و منطقية تشكل نموذج النظام.
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The Electromagnetic Interference and EMC are one important phenomenon, since the EMI
causes degradation in the performance of electric and electronic instruments.
The EMI- problem may decrease effectiveness of sensitive devices and even may lead to
a
failure of its operation.
This paper studies EMI-problem between different systems by using convenient computer
programs as CST, which provides modulation and simulation of this problem. This method provide
ability to trace and evaluate EMI microscopically in space and real time.
Neural topic models can augment or replace bag-of-words inputs with the learned representations of deep pre-trained transformer-based word prediction models. One added benefit when using representations from multilingual models is that they facilitat
e zero-shot polylingual topic modeling. However, while it has been widely observed that pre-trained embeddings should be fine-tuned to a given task, it is not immediately clear what supervision should look like for an unsupervised task such as topic modeling. Thus, we propose several methods for fine-tuning encoders to improve both monolingual and zero-shot polylingual neural topic modeling. We consider fine-tuning on auxiliary tasks, constructing a new topic classification task, integrating the topic classification objective directly into topic model training, and continued pre-training. We find that fine-tuning encoder representations on topic classification and integrating the topic classification task directly into topic modeling improves topic quality, and that fine-tuning encoder representations on any task is the most important factor for facilitating cross-lingual transfer.
حظي مؤخرا اختصاص البيانات الضخمة باهتمام كبير في مجالات متنوعة منها (الطب , العلوم , الادارة, السياسة , ......)
و يهتم هذا الاختصاص بدراسة مجموعة البيانات الضخمة والتي تعجز الادوات والطرق الشائعة على معالجتها و ادارتها و تنظيمها خلال فترة زمنية مقبو
لة و بناء نموذج للتعامل مع هذه المعطيات والتنبؤ باغراض مطلوبة منها.
ولاجراء هذه الدراسات ظهرت طرق عدة منها النماذج التي تعتمد على مجموعة من البيانات و نماذج تعتمد على المحاكاة و في هذه المقالة تم توضيح الفرق بين النموذجين و تطبيق نهج جديد يعتمد على التكامل بين النموذجين لاعطاء نموذح افضل لمعالجة مسالة البيوت البلاستيكة
Importance and aims of the research
Biomechanics science is interested in studying the dynamic function and the
movement of vital tissues depending on their mechanical properties.
The main objective of this research is to design a digital model of
the human femur
using the engineering software specialized in medical image processing and engineering
design in order to simulate the mechanical behavior. This would provide important medical
information to orthopedic surgeons concerning the paths and the causes of bone fractures
and deformities, and open a new perspectives in prosthetics efficiency enhancement.
Materials and methods
A three dimensional digital model of the femur was produced using the software
DeVIDE v 9.8 for medical image processing. A surface triangular mesh was constructed
and the mechanical response of the model has been simulated using Ansys 14.5.
Results and discussions
We have shown the steps necessary to design a computerized model of femur bone
on the basis of three-dimensional X-ray images. The results showed the distribution of
stresses and displacements of human femur at normal load conditions.
Conclusion and recommendations
It is recommended to adopt the specialized engineering software for the threedimensional
simulation which can be used in different medical applications.
Topic models are useful tools for analyzing and interpreting the main underlying themes of large corpora of text. Most topic models rely on word co-occurrence for computing a topic, i.e., a weighted set of words that together represent a high-level s
emantic concept. In this paper, we propose a new light-weight Self-Supervised Neural Topic Model (SNTM) that learns a rich context by learning a topic representation jointly from three co-occurring words and a document that the triple originates from. Our experimental results indicate that our proposed neural topic model, SNTM, outperforms previously existing topic models in coherence metrics as well as document clustering accuracy. Moreover, apart from the topic coherence and clustering performance, the proposed neural topic model has a number of advantages, namely, being computationally efficient and easy to train.
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